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Time-scale decomposition of an optimal control problem in greenhouse climate management

Based on differences in dynamic response times in the crop production process, a hierarchical decomposition of greenhouse climate management is proposed. To a large extent the proposed decomposition builds on the time-scale decomposition of singularly perturbed systems commonly found in the literature. Main difference with these existing theoretical concepts is that the proposed decomposition is able to deal with rapidly fluctuating deterministic external inputs or disturbances acting on the fast sub-processes. For an example of economic optimal greenhouse climate management during one lettuce production cycle, the decomposition was successfully evaluated in simulations. Using these favourable results, a hierarchical concept for economic optimal greenhouse climate management is derived and discussed in view of application in horticultural practice.
- Wageningen University & Research Netherlands
co2 enrichment, Wageningen UR Glastuinbouw, carbon-dioxide, optimal-control strategies, lettuce, Leerstoelgroep Agrarische bedrijfstechnologie, tomato crops, optimization
co2 enrichment, Wageningen UR Glastuinbouw, carbon-dioxide, optimal-control strategies, lettuce, Leerstoelgroep Agrarische bedrijfstechnologie, tomato crops, optimization
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).97 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
